A max metric to evaluate a cluster

Authors:
Hosein Alizadeh;Hamid Parvin;Sajad Parvin;Zahra Rezaei;Moslem Mohamadi
Affiliations:
Islamic Azad University, Mahdi Shahr, Iran;Islamic Azad University, Mamasani Nourabad, Iran;Islamic Azad University, Mamasani Nourabad, Iran;Islamic Azad University, Mamasani Nourabad, Iran;Islamic Azad University, Mamasani Nourabad, Iran
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Year:
2012

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Abstract

In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. The clusters which satisfy a threshold of the proposed measure are selected to participate in clustering ensemble. To combine the chosen clusters, some methods are employed as aggregators. Employing this new cluster validation criterion, the obtained ensemble is evaluated on some well-known and standard datasets. The empirical studies show promising results for the ensemble obtained using the proposed criterion comparing with the ensemble obtained using the standard clusters validation criterion. Besides to reach the best results, the method gives an algorithm based on which one can find how to select the best subset of clusters from a pool of clusters.